Multiclass covert speech classification using extreme learning machine

• Published in: Biomedical engineering letters Vol. 10; no. 2; pp. 217 - 226
• Main Authors: Pawar, Dipti, Dhage, Sudhir
• Format: Journal Article
• Language: English
• Published: Korea The Korean Society of Medical and Biological Engineering 01.05.2020; Springer Nature B.V
• Subjects: Algorithms; Artificial neural networks; Biological and Medical Physics; Biomedical Engineering and Bioengineering; Biomedicine; Biophysics; Classification; EEG; Electroencephalography; Engineering; Interfaces; Kernels; Learning algorithms; Machine learning; Medical and Radiation Physics; Original; Original Article; Signal classification; Speech; Words (language); Covert speech; Electroencephalography (EEG); Wavelet transform; Multiclass classification; Brain–computer interface (BCI)
• ISSN: 2093-9868; 2093-985X; 2093-985X
• DOI: 10.1007/s13534-020-00152-x

The objective of the proposed research is to classify electroencephalography (EEG) data of covert speech words. Six subjects were asked to perform covert speech tasks i.e mental repetition of four different words i.e ‘left’, ‘right’, ‘up’ and ‘down’. Fifty trials for each word recorded for every subject. Kernel-based Extreme Learning Machine (kernel ELM) was used for multiclass and binary classification of EEG signals of covert speech words. We achieved a maximum multiclass and binary classification accuracy of (49.77%) and (85.57%) respectively. The kernel ELM achieves significantly higher accuracy compared to some of the most commonly used classification algorithms in Brain–Computer Interfaces (BCIs). Our findings suggested that covert speech EEG signals could be successfully classified using kernel ELM. This research involving the classification of covert speech words potentially leading to real-time silent speech BCI research.